Using Novus Event Analysis to Tackle Behavioral Biases

There are two schools of thought regarding our industry. One is that alpha still exists in the market, and those with the greatest investment acumen are best able to capture it. Another is that the opportunities to generate alpha have declined drastically over the years due to how fast information travels and gets absorbed by market participants. Our data provides evidence supporting the latter school of thought, as we outlined last year.

If you trust the evidence, it means that we’re getting closer to what the efficient market hypothesis proposes: at any given moment in a highly liquid market, security prices reflect all available information, leaving little-to-no room for alpha generation. We agree with the evidence and its conclusion, but only in regards to exogenous alpha sources; i.e., opportunities that are associated with pricing inefficiencies due to information asymmetry related to securities.

The study of behavioral finance suggests that there are other, endogenous, sources of alpha. These are related to the fact that investors are not rational beings, and have psychological influences and biases that affect their investment decisions (more on this here). Our assessment is that opportunities for endogenous alpha are still abundant!

Because of their nature, behavioral biases tend to affect portfolio construction rather than security selection. In this article, we examine the importance of identifying behavioral biases, present examples of how Novus can help investors identify their own biases, and quantify the impact of identifying these opportunities.

How is your timing when entering a position?

Using Novus Public Ownership data and the Event Analysis toolkit, we identified a manager whose position entry timing has consistently been at the tail end of stock price rallies. The manager’s average holding length of long positions within the past 15 years has been approximately 30 months.

Figure 1 — We determine the average holding length of an example manager using public data sources.

In Figure 2, we observe that in the 30 months before a position entry, securities exhibited on average a 78% increase in total return.

Figure 2 — Event Analysis reveals that this manager loses out on most of the gains because they consistently buy late.

After an entry, securities exhibited a mere 17% increase. Granted, in hindsight finance has 20/20 vision, but given the statistical significance of this test, we can conclude that the manager is showing a persistent behavioral bias that is detrimental to returns.

Hypothetically, if the manager had entered these same positions 10 months earlier, the securities would have returned approximately 34%, thus adding 17 p.p. of incremental profit to investors, as seen in Figure 3.

Figure 3 — A hypothetical scenario where the manager enters all positions 10 months earlier, a decision which would have doubled returns.

One could argue at this point that, since markets tend to go up on average, looking at total returns isn't totally fair. Thanks to the flexibility of the Novus Event Analysis toolkit—and to further test our hypothesis—we can analyze security returns relative to their respective benchmarks. It may very well be possible that examining security performance on an absolute basis after entries tells us a completely different story compared to a relative basis.

After adjusting our model to analyze security active return, we observe that in the 30 months before a position entry, securities exhibited on average a 29% increase in return relative to their benchmarks.

In the 30 months after entries, those securities exhibited a 3% decrease in active return. Re-casting data this way highlights that the bias is actually worse than we thought. This finding helps us validate that the manager’s position entry timing decisions have not been accretive to returns or alpha.

How is your timing when exiting a position?

It is not unusual to find a manager who enters positions at overvalued prices and holds on to them too long in hopes of justifying their decision. (The phenomenon of illogically preferring that which you already own over something you could own is known as Endowment Bias.) In several clicks, we are able to change the event we are analyzing from entries to exits, and analyze the output. Interestingly, this manager does a slightly better job at timing exits compared to entries.

Figure 5 — Analyzing position exits this time, we can see the manger captures a majority of the returns before exiting.

On average, in the 30 months before a position exit, securities exhibited a 64% increase in total return. After an exit, securities exhibited a 20% increase in total return.

But is this manager still exiting his positions late given that securities continue to increase in price?

To test this, we shifted the event 10 months forward. As we do so, we observe that prior to the new exit date, securities exhibited only a 32% increase in returns. Additionally, 22% of the increase prior to exits came from active return, whereas after exits there was a 1.20% decrease in active return.

Figure 6 — In the hypothetical scenario where we shift position exits to be 10 months later.

It is evident that the decisions that the manager made regarding position exits are accretive to alpha.

Measure, adjust, and monitor.

Based on our work over the last decade, we calculated that behavioral biases, when addressed, can add between 300-500 bps of alpha in 80% of cases. Given the well documented decline in exogenous alpha sources, this opportunity is too large to ignore.

Two challenges usually impede investors from taking action here. First, performing analyses in a way that’s flexible enough to provide a framework capable of handling the spectrum of cases. Second, putting yourself through the change management program needed to affect lasting behavioral change.

Novus can help with both:

Using Event Analysis, we were just able to evaluate a manager’s bias around chasing trends on entries, and also found a strength in superior timing of exits. We can test this using benchmarks. We could have gone on to test how the manager behaved during drawdowns, earnings upgrades, and other situations where biases typically occur. It’s worth emphasizing that simply because you can calculate something, it doesn’t mean it’s useful. That is why the Event Analysis framework, pioneered by Novus, comes with rigorous statistical testing—to ensure you extract the signal from the noise.

After you’ve committed to a behavioral change, Novus can help you spot behavioral biases on the horizon, catching opportunities earlier. You can now configure Novus Alerts to notify you as soon as positions in your portfolio exhibit patterns worthy of attention (e.g., losing positions held too long), thus securing extra awareness around critical movements in your portfolio.

If you’d like to learn more about quantifying your behavioral biases, or staying on top of your portfolio with alerts, we'd be happy to chat.